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Deep Learning: Recurrent Neural Networks with Python
INTRODUCTION
Introduction to AI Sciences
Focus of the Course
APPLICATIONS OF RNN
Human Activity Recognition
Image Captioning
Machine Translation
Speech Recognition
Stock Price Predictions
When to Model RNN
Activity
DEEP NEURAL NETWORK (DNN) OVERVIEW
Introduction to Deep Learning Module
Neuron and Perception
DNN Architecture
FeedForward FullyConnected MLP
Calculating Number of Weights of DNN
Number of Neurons Versus Number of Layers
Discriminative Versus Generative Learning
Universal Approximation Theorem
Why Depth
Decision Boundary in DNN
Bias Term
Activation Function
DNN Training Parameters
Gradient Descent
Backpropagation
Training DNN Animation
Weight Initialization
Batch MiniBatch Stocastic
Batch Normalization
Rprop Momentum
Convergence Animation
DropOut EarlyStopping Hyperparameters
RNN ARCHITECTURE
Introduction
Fixed Length Memory Model
Infinite Memory Architecture
Weight Sharing
Notations
Many-To-Many Model
Many-To-One Model
One-To-Many Model
Activity: Many-to-One
Many-To-Many Different Sizes Model
Activity: Many to Many
Models Summary
Deep RNNs
GRADIENT DESCENT IN RNN
Introduction
Example Setup
Equations
Loss Function
Why Gradients
Chain Rule
Chain Rule in Action
Backpropagation Through Time
Activity
VANISHING GRADIENTS IN RNN
Introduction to a Better RNN Module
Introduction to Vanishing Gradients in RNN
Gated Recurrent Unit (GRU)
Gated Recurrent Unit (GRU) Equations
Long Short Term Memory (LSTM)
Long Short Term Memory (LSTM) Equations
Bidirectional Recurrent Neural Networks
Attention Model
Attention Model Equation
TENSORFLOW
Introduction
TensorFlow Text Classification Example using RNNs
PROJECT 1: BOOK WRITER
Introduction
Data Mapping
Modelling RNN Architecture
Modelling RNN Model in TensorFlow
Modelling RNN Model Training
Modelling RNN Model Text Generation
Activity
PROJECT 2: STOCK PRICE PREDICTION
Problem Statement
Dataset
Data Preparation
RNN Model Training and Evaluation
Activity
FURTHER READING AND RESOURCES
Further Reading and Resources
Introduction to Vanishing Gradients in RNN
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